{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "f = open('task_2_lianjia_data.csv',encoding='UTF-8') \n",
    "lianjia_data = pd.read_csv(f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "  简介（方式·小区名 户型 朝向）   区 地铁站     村  面积大小（㎡） 朝向      户型  \\\n0   整租·长桥一村 1室0厅 南  徐汇  长桥  长桥一村       39  南  1室0厅1卫   \n1   整租·馨宁公寓 1室1厅 南  徐汇  华泾  馨宁公寓       42  南  1室1厅1卫   \n2   整租·长桥三村 2室1厅 南  徐汇  长桥  长桥三村       51  南  2室1厅1卫   \n\n                          楼层类型  楼层（层）       标签  \n0  高楼层                              6     随时看房  \n1  高楼层                             29  精装,随时看房  \n2  高楼层                              6     随时看房  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>简介（方式·小区名 户型 朝向）</th>\n      <th>区</th>\n      <th>地铁站</th>\n      <th>村</th>\n      <th>面积大小（㎡）</th>\n      <th>朝向</th>\n      <th>户型</th>\n      <th>楼层类型</th>\n      <th>楼层（层）</th>\n      <th>标签</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>整租·长桥一村 1室0厅 南</td>\n      <td>徐汇</td>\n      <td>长桥</td>\n      <td>长桥一村</td>\n      <td>39</td>\n      <td>南</td>\n      <td>1室0厅1卫</td>\n      <td>高楼层</td>\n      <td>6</td>\n      <td>随时看房</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>整租·馨宁公寓 1室1厅 南</td>\n      <td>徐汇</td>\n      <td>华泾</td>\n      <td>馨宁公寓</td>\n      <td>42</td>\n      <td>南</td>\n      <td>1室1厅1卫</td>\n      <td>高楼层</td>\n      <td>29</td>\n      <td>精装,随时看房</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>整租·长桥三村 2室1厅 南</td>\n      <td>徐汇</td>\n      <td>长桥</td>\n      <td>长桥三村</td>\n      <td>51</td>\n      <td>南</td>\n      <td>2室1厅1卫</td>\n      <td>高楼层</td>\n      <td>6</td>\n      <td>随时看房</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 3
    }
   ],
   "source": [
    "lianjia_data.head(3)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [],
   "source": [
    "#分组聚合\n",
    "lianjia_data_gb_area=lianjia_data.groupby('面积大小（㎡）').agg('count')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "#转换为列表\n",
    "area_list=lianjia_data_gb_area.index.tolist()\n",
    "area_count_list=lianjia_data_gb_area['区'].tolist()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##画图\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False#显示中文\n",
    "plt.title('area and count')\n",
    "plt.plot(area_list, area_count_list, 'bo-')\n",
    "plt.show()\n",
    "plt.title('area and count')\n",
    "plt.bar(area_list, area_count_list, alpha=0.9, width =1, facecolor = 'red', edgecolor = 'red', label='one', lw=1)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "由图可见，面积大小在25-50区间的房源较多，其余房源较少"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##按照区显示房源数量\n",
    "lianjia_data_gb_location=lianjia_data.groupby('区').agg('count')\n",
    "location_list=lianjia_data_gb_location.index.tolist()\n",
    "location_count_list=lianjia_data_gb_location['村'].tolist()\n",
    "plt.title('location and count')\n",
    "plt.bar(location_list, location_count_list, alpha=0.9, width =1, facecolor = 'red', edgecolor = 'red', label='one', lw=1)\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "由条形图可以看出，奉贤、嘉定等地房源数量较少，徐汇、静安房源数量较多。"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#按照楼层类型显示房源数量\n",
    "lianjia_data_gb_floor=lianjia_data.groupby('楼层类型').agg('count')\n",
    "floor_list=lianjia_data_gb_floor.index.tolist()\n",
    "floor_count_list=lianjia_data_gb_floor['村'].tolist()\n",
    "plt.title('floor and count')\n",
    "plt.pie(floor_count_list, labels=floor_list,)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "由图可见，高楼层房源数量较多，地下室房源较少。\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}